Together AI is a strong option for hosted open-weight models, and it is often the first place developers look when leaving OpenAI. This guide covers what Together AI does well, where a Chinese model API gateway fits differently, and how to compare the two honestly — including the parts that are not about price.
Together AI hosts a large catalogue of open-weight models — Llama, Mistral, Qwen and many others — behind an OpenAI-compatible API, with dedicated endpoints and fine-tuning available. If your requirement is “run a specific open-weight checkpoint without managing GPUs”, that is squarely what the platform is for. Their catalogue and current rates are documented at docs.together.ai.
The distinction is not better-versus-worse; it is catalogue and access.
Live rates from the AIWave pricing endpoint:
| Model ID | Input / 1M tokens | Output / 1M tokens |
|---|---|---|
deepseek-v4-flash | $0.638 | $1.914 |
deepseek-v4-pro | $1.914 | $5.742 |
glm-4.7-flash | starter credits | starter credits |
glm-5 | $1.55 | $4.96 |
kimi-k2.5 | $0.66 | $3.30 |
qwen3-coder-480b-a35b-instruct | $0.12 | $0.36 |
ernie-4.0-turbo-8k | $0.0012 | $0.0012 |
Rates read from the AIWave pricing endpoint on 2026-07-26. Check live pricing before budgeting — providers revise rates.
We are not printing a competitor price table here, because third-party pricing changes without notice and a stale comparison is worse than none. Read Together AI’s rates from their own pricing page and compare against the live figures above.
Because both platforms speak the OpenAI wire format, running this comparison costs you a
base_url change, not an integration project.
from openai import OpenAI
def ask(base_url, api_key, model, prompt):
client = OpenAI(api_key=api_key, base_url=base_url)
r = client.chat.completions.create(
model=model,
messages=[{"role": "user", "content": prompt}],
temperature=0,
)
return r.choices[0].message.content, r.usage.total_tokens
# same function, two providers
print(ask("https://aiwave.live/v1", AIWAVE_KEY, "deepseek-v4-pro", PROMPT))Nothing forces a single choice. A common arrangement is to route by capability — open-weight models where you need a specific checkpoint, Chinese models where price or capability wins — behind one internal interface. Building a multi-model router covers that pattern, and error handling explains how to fail over cleanly when one provider degrades.
It depends entirely on the model pair you compare and changes as both sides revise pricing. Price your own token volume against each provider’s current published rates rather than relying on a comparison table with a date on it.
Yes, if both endpoints are OpenAI-compatible. Swap base_url and the model name.
Not through AIWave. Email or GitHub signup is sufficient, and billing is in USD.
Every claim above traces back to one of these. Pricing figures come from the AIWave pricing endpoint on 2026-07-26; capability claims come from the vendors’ own documentation.